Designing conversational systems driven by a semantic network with a library of templated query operators

ABSTRACT

A method, computer system, and computer program product for a conversational system driven by a semantic network with a library of templated query operators are provided. The embodiment may include loading one or more operators for the conversational system to the library of templated query operators. The embodiment may also include receiving a query statement from a user. The embodiment may further include identifying an operator from the library to process the received query. The embodiment may also include identifying one or more input terms for the identified operator within the received query. The embodiment may further include generating one or more output terms based on processing the one or more identified input terms using the identified operator. The embodiment may also include generating a natural language response to the received query based on the one or more generated output terms.

BACKGROUND

The present invention relates, generally, to the field of computing, andmore particularly to conversational systems.

Conversational systems relate to software programs capable of receivingand analyzing human speech to perform an action and returning a responsein a coherent structure. Some conversational systems integrate variousspeech recognition, natural language processing, and dialogunderstanding technologies to perform necessary tasks. Some typical usesof conversational systems include virtual assistants, customer caresystems, and the Internet of Things.

SUMMARY

According to one embodiment, a method, computer system, and computerprogram product for a conversational system driven by a semantic networkwith a library of templated query operators are provided. The embodimentmay include loading one or more operators for the conversational systemto the library of templated query operators. The embodiment may alsoinclude receiving a query statement from a user. The embodiment mayfurther include identifying an operator from the library to process thereceived query. The embodiment may also include identifying one or moreinput terms for the identified operator within the received query. Theembodiment may further include generating one or more output terms basedon processing the one or more identified input terms using theidentified operator. The embodiment may also include generating anatural language response to the received query based on the one or moregenerated output terms.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates an exemplary networked computer environment accordingto at least one embodiment;

FIG. 2 is an operational flowchart illustrating a templated queryoperator process according to at least one embodiment;

FIG. 3 is a functional block diagram of utilizing templated queryoperators in a conversational system according to at least oneembodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. In the description, details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the presented embodiments.

Embodiments of the present invention relate to the field of computing,and more particularly to conversational systems. The following describedexemplary embodiments provide a system, method, and program product to,among other things, utilize a library of templated query operators toconvert a natural language input to a query on a semantic network.Therefore, the present embodiment has the capacity to improve thetechnical field of conversational systems by reducing resources andincreasing efficiency during the process of developing a conversationalsystem.

As previously described, conversational systems relate to softwareprograms capable of receiving and analyzing human speech to perform anaction and returning a response in a coherent structure. Someconversational systems integrate various speech recognition, naturallanguage processing, and dialog understanding technologies to performnecessary tasks. Some typical uses of conversational systems includevirtual assistants, customer care systems, and the Internet of Things.

Platforms to design conversational systems are becoming more prevalentquickly in the developer environment. A crucial technical challenge inthe development of conversational systems is to partition thedevelopment effort between horizontal platform features and verticalsolution features. For example, natural language understanding andnatural language generation may each be mostly a platform featureconfigured for a solution whereas dialogue scripting may be mostly asolution feature requiring templating dialog specific to a solution.

A semantic network represents knowledge as a network that relatesconcepts with semantic relations. A semantic network may be used when anindividual possesses knowledge of related concepts. For example, in thesentences “A whale is a mammal” and “A whale live in water”, the word“whale” may be semantically related to both the words “mammal” and“water” in a network. However, the words “water” and “mammal” may not beconnected since there is not related concept directly connecting the twowords. Many semantic networks are cognitively-based in that some senseof rationale may be needed to connect relations to each other.

For a conversational system, a semantic network may function as both amemory store to remember all known facts as well as a reasoning on knownfacts. Conversational systems driven by semantic networks may have anumber of useful advantages. For example, semantic network-drivenconversational systems may partially decouple the design of theconversational frontend from the backend of curating and normalizingdata that populates the semantic network. Similarly, conversationalsystems driven by semantic networks may improve performance capabilitiesby simply upgrading the semantic network. Additionally, such systems maygreatly generalize the class of possible conversations. As such, it maybe advantageous to, among other things, provide a library of templatedquery operators which enable complex natural language-based interactionsbetween a user and a semantic network representing the knowledge of theconversational system.

According to one embodiment, a set of operators may be imported from alibrary for a conversational system. When a natural language input isreceived from a user into a conversational system, a natural languageclassifier may be implemented that is capable of identifying thespecific operator loaded from the library appropriate to process thenatural language input. A natural language understanding module may thenbe utilized to collect the input variables required for the identifiedoperator based on the ontology of the semantic networks. A querytranslator may convert the input for the operator to a specific queryfor the data store format of the semantic network. Once the query isprocessed by the imported operator, a natural language generation modulemay generate a natural language output to present to the user.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, method,and program product to implement a library of templated query operatorsin a semantically-driven conversational system.

Referring to FIG. 1, an exemplary networked computer environment 100 isdepicted, according to at least one embodiment. The networked computerenvironment 100 may include client computing device 102 and a server 112interconnected via a communication network 114. According to at leastone implementation, the networked computer environment 100 may include aplurality of client computing devices 102 and servers 112 of which onlyone of each is shown for illustrative brevity.

The communication network 114 may include various types of communicationnetworks, such as a wide area network (WAN), local area network (LAN), atelecommunication network, a wireless network, a public switched networkand/or a satellite network. The communication network 114 may includeconnections, such as wire, wireless communication links, or fiber opticcables. It may be appreciated that FIG. 1 provides only an illustrationof one implementation and does not imply any limitations with regard tothe environments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

Client computing device 102 may include a processor 104 and a datastorage device 106 that is enabled to host and run a software program108 and a templated query operator program 110A and communicate with theserver 112 via the communication network 114, in accordance with oneembodiment of the invention. Client computing device 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer, orany type of computing device capable of running a program and accessinga network. As will be discussed with reference to FIG. 4, the clientcomputing device 102 may include internal components 402 a and externalcomponents 404 a, respectively.

The server computer 112 may be a laptop computer, netbook computer,personal computer (PC), a desktop computer, or any programmableelectronic device or any network of programmable electronic devicescapable of hosting and running a templated query operator program 110Band a database 116 and communicating with the client computing device102 via the communication network 114, in accordance with embodiments ofthe invention. As will be discussed with reference to FIG. 4, the servercomputer 112 may include internal components 402 b and externalcomponents 404 b, respectively. The server 112 may also operate in acloud computing service model, such as Software as a Service (SaaS),Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). Theserver 112 may also be located in a cloud computing deployment model,such as a private cloud, community cloud, public cloud, or hybrid cloud.

According to the present embodiment, the templated query operatorprogram 110A, 110B may be a program capable of importing an operatorfrom a library based on user query terms. The templated query operatorprogram 110A, 110B may also be capable processing the query termsrequired for the imported operator to properly execute the query search,and generate a natural language response to be presented to a user. Thetemplated query operator program 110A, 110B may utilize an intentclassifier, an input collector, and a natural language generator tocomplete each step. The templated query operator method is explained infurther detail below with respect to FIG. 2.

FIG. 2 is an operational flowchart illustrating a templated queryoperator process 200 according to at least one embodiment. At 202, thetemplated query operator program 110A, 110B loading one or moreoperators for a conversational system to a library of templated queryoperators. The library of templated operators may be expected to capturesome of the common motifs in conversational turns involving queryingknowledge bases. There may be various categories of operators that aretrained to handle different use cases, such as informational and processoperators, problem solving operators, and recommending operators.Informational and process operators may identify facts or documents thatmatch a given set of features, such as a topical question operator and ahint operator discussed in more detail below. Inputs for information andprocess operators may be a set of constrains given as relations and/orrelation-concept tuples and outputs for these operators may be a set ofconcepts which satisfy the constraints. Problem solving operators mayinvoke a root-cause analysis which, given a set of inferred symptoms,identifies a common explanation. Problem solving operators may havecross-cutting applications in bots for tech support and medicalassistance, such as a common relations operator, discussed in moredetail below. Recommending operators may be capable of recommending aproduct or service given specific user preferences, such as arecommender operator discussed in more detail below.

The topical question operator may be a topical question operator thatcan generate questions in an automated education bot. Each generatedquestion can be personalized to the user and can also be refashioned forother bots. The topical question operator may receive as inputs arelation r, a relation-concept pair (s,A), and a set of annotationswhile providing as an output a fact tuple (X,r,Z) satisfying (Z,s,A) andannotations. For example, the topical question operator may receive, asan input, the natural language sentence “Ask an unanswered question onthe capital of a country in Europe” from a user. The input values maybe: relation r=capital, relation-concept pair (country_in, Europe), andannotations “asked=no”. Therefore, the topical question operator mayoutput the function:

{(Paris,capital,France), asked=no}

(France,country_in,Europe)

which may result in a natural language response provided to the user of“What is the capital of France?”.

The hint operator may be used as an educational bot to provide a hint toa given question. The provided hint may be specialized to the topic ofthe question, and can be personalized to the information already knownby the user. The hint operator may received as inputs a facttuple=(A,r,B), a question concept=A, and annotations={(Ann1:Ann1Val,Ann2:Ann2Val, . . . )} while providing as an output a tuple (A,s,C)satisfying all input annotations. For example, when a conversationalsystem is engaging a user and has asked the question “What is thecapital of France?”, the user may respond with the phrase “Show me ahint”, which may be used as the input phrase for the hint operator. Thehint operator may identify the input values of:

{(Paris, capital,France), “Difficulty:Easy”, “Topic: Geography,Politics”, “Asked:No”} Annotations={Topic: Geography,Answered: Yes}

Therefore, the hint operator may output the function:

{(Seine,flows through, Paris), “Topic:Geography”, “Asked:Yes”,“Answered:Yes”}

which may result in a natural language response provided to the user of“Here is a hint based on an earlier question you answered: Seine flowsthrough this.”.

The recommender operator may recommend a product or service to a userbased on a given set of features, and can be configured to includeadditional features based on the commercial intent. The recommenderoperator may receive as inputs a set of relation-concept tuples={(p,A),(q,B), (r,C), . . . } and a relation r and a concept Y while providingan output of a fact tuple (X,r,Z) satisfying (Z,*,Y). For example, therecommender operator may receive from a user the natural languagestatement “Show me holiday in beach destination that are cheap”. Therecommender operator may identify the input values of:

{(located_in,Beach), (price, law)}

Relation r=“combo_deal”. Concept Y=“flight”

Therefore, the recommender operator may output the function:

Bali {(Bali,located_in,Beach), (Bali,price,Low)}

{(Bali, combo_deal,IndonesianAirlines)}

{(IndonesianAirlines,is,flight)}

which may result in a natural language response provided to the user of“You should go to Bali. We have a combo deal with Indonesian Airlines.”.

The common relations operator may be used in a conversational commercebot to classify user preferences. Additionally, the common relationsoperator may be used in a cognitive finance application to ask questionson common features of companies, deal, or transactions. The commonrelations operator may receive as inputs a set of concepts {A,B,C, . . .} and return as outputs relation-concept pairs (r,Z) that each satify(A,r,Z), (B,r,Z), (C,r,Z), . . . sets. For example, in response to aconversational system asking the user “Which phones did you own?” andthe user responding “Note 2®, Note 4®, and Nexus 6p®” (Galaxy Note 2,Galaxy Note 4 and all Galaxy-based trademarks and logos are trademarksor registered trademarks of Samsung Electronics and/or its affiliates)(Nexus 6P and all Nexus-based trademarks and logos are trademarks orregistered trademarks of Google Inc. and/or its affiliates), the commonrelations operator may identify as inputs {Note2,Note4,Nexus6P}.Therefore, the common relations operator may output the function:

(running_on,Android)

(screen_size,large)

Which may result in a natural language response provided to the user of“Looks like you like phones running on an Android® operating system andwith screen sizes that are large. Shall I show you more such phones?”(Android and all Android-based trademarks and logos are trademarks orregistered trademarks of Google Inc. and/or its affiliates).

Each operator added to the library may have a corresponding semanticnetwork query module that can consume a semantic network with anontology of concepts and relations, and convert a natural language querywith annotations of semantic roles to a data-store dependent query andreturn valid results. When added to the library, the templated queryoperator program 110A, 110B may require various criteria, such as theinterface of operators (e.g., inputs and outputs), a list of exampletrigger phrases that may invoke the operator by an intent classifier,example operator entity mappings (i.e., user text to operator inputs) totrain the input collector, and examples of natural language generationfrom the output.

Each input for an operator may have two attributes: a source and a type.The source may be a user input, a context, or a configuration. The typemay be an annotation or a tuple involving concepts and relations, suchas “concept”, “relation”, “concept,relation”, “concept,relation,concept”, etc. Each output for an operator may have a tupleinvolving concepts and relations, such as “concept”, “relation”,“concept,relation”, “concept, relation,concept”, etc.

The query operators may form a subset of conversations in which the usercan engage. The intent classifier may classify which user input classifyas invocation for a specific query operator. For example, a questionoperator may have trigger phrases of “ask me a question on . . . ”,“quiz me on . . . ”, and “test me about . . . ”. A platform developermay be required to identify examples of trigger phrases for the intentclassifier to properly operate. In at least one embodiment, thetemplated query operator program 110A, 110B may utilize machine learningto identify trigger phrases not established by a platform developerduring import of an operator.

Then, at 204, the templated query operator program 110A, 110B receive aquery from a user. During an exchange with a conversational system, auser may ask a natural language query to or make a statement to theconversational system, such as “Where can I purchase shoes near me?” or“Show my inexpensive travel destinations”.

Next, at 206, the templated query operator program 110A, 110B identifiesan operator to process the received query. Once a query is received, thetemplated query operator program 110A, 110B may analyze the receivedquery to identify an appropriate operator capable of processing thequery and returning accurate results. To properly identify acorresponding operator, the templated query operator program 110A, 110Bmay utilize an intent classifier that may be capable of determine userintent in the receive query statement by applying natural languageprocessing techniques, such as semantic analysis. For example, thetemplated query operator program 110A, 110B, implementing the intentclassifier, may determine the user query of “Ask an unanswered questionon a capital of a country is Europe.” is asking for a question to begenerated and presented to the user that relates to a European capital.Therefore, the templated query operator program 110A, 110B may determinea topical question operator is most appropriate in handling the receivedquery based on the determined user intent.

Then, at 208, the templated query operator program 110A, 110B identifiesone or more input terms for the identified operator within the receivedquery. When establishing each operator, a platform developer may berequired to provide examples that map entities in the user text to theinputs expected in the operator interface. By performing supervisedtraining, the conversation system may be capable of learning a model tomap for a new user input. For example, if an interface has a relation r,a relation-concept pair (s,A), and a set of annotations established asinputs and a user inputs statement of “Ask an unanswered question on acapital of a country in Europe”, the templated query operator program110A, 110B, using a semantic query module, may identify “question” as aconcept, “unanswered” as an annotation, “capital of” as a relation,“country in” as a relation, and “Europe” as a concept. Therefore, themapping of each identification to the interface may be performed by thedeveloper as relation r is “capital of”, relation-concept pair (s,A) is(“country in”, “Europe”), and the annotation set is “unanswered”.

In at least one embodiment, the templated query operator program 110A,110B may utilize an input collector to identify the input terms from thereceived query. The input collector may provide rules and be capable ofnatural language understanding to collect the required input terms for aspecific operator from the input text. The type of input collectedutilized by the templated query operator program 110A, 110B may dependon the ontology behind the semantic network.

Next, at 210, the templated query operator program 110A, 110B generatesone or more output terms based on processing the one or more identifiedinput terms using the identified operator. Once the input terms areidentified, the templated query operator program 110A, 110B may use theinput terms within the identified operator to generate one or moreoutput terms. For example, in the previous example where the user inputterms were identified as relation r being “capital of”, relation-conceptpair (s,A) being (“country in”, “Europe”), and the annotation set being“unanswered” for the user query statement “Ask an unanswered question ona capital of a country in Europe.”, the templated query operator program110A, 110B utilizing the semantic network-driven conversational systemmay produce the output terms (Paris, capital,France).

Then, at 212, the templated query operator program 110A, 110B generatesa natural language response for the received query based on the one ormore generated output terms. Using a natural language generator, thetemplated query operator program 110A, 110B may analyze the output termsto generate a natural language response for the user. For example, ifthe output terms in the previous example are (Paris,capital,France) forthe initial user query statement of “Ask an unanswered question on acapital of a country in Europe.”, the templated query operator program110A, 110B, utilizing a natural language generator, may produce thenatural language response “What is the capital of France?”. The naturallanguage generator may provide rules to generate natural language textfrom the output terms generated by running the query of the identifiedoperator.

In at least one embodiment, the templated query operator program 110A,110B may transmit the natural language response to a graphical userinterface of a client computing device 102 to allow the user to view thenatural language response. In at least one other embodiment, thetemplated query operator program 110A, 110B may present the naturallanguage response to the user as a human voice through a speaker usingnatural language processing techniques, such as text-to-speech software.

Referring now to FIG. 3, a functional block diagram 300 of utilizingtemplated query operators in a conversational system is depicted,according to at least one embodiment. A library of templates 302 maycontain a group of operators, such as hint operator 304, topicalquestion operator 306, recommender operator 308, and common relationsoperator 310. When a user engages with a trivia conversational system312 that is driven by a semantic network of trivia 314, the user maypose a query statement to the trivia conversational system 312. Thequestion may be analyzed by an intent classifier 318 and, based onpreconfigured trigger phrases, the intent classifier 318 may determinethe topical question operator 306 is most appropriate to process theuser query statement. The user query statement may then be analyzed byan input collector 320 to identify input terms within the user querystatement needed to process the statement using the topical questionoperator 306. A query translator 322 may be utilized to convert theidentified inputs to a specific query for the data store format of thesemantic network of trivia 314. Once converted, the query translator 322may transmit the query to a natural language query engine 316 within thesemantic network of trivia 314 to produce an output from the topicalquestion operator 306. The output may then be translated to naturallanguage using a natural language generator 324 and, subsequentlypresented to the user.

It may be appreciated that FIGS. 2 and 3 provide only an illustration ofone implementation and do not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components of theclient computing device 102 and the server 112 depicted in FIG. 1 inaccordance with an embodiment of the present invention. It should beappreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The data processing system 402, 404 is representative of any electronicdevice capable of executing machine-readable program instructions. Thedata processing system 402, 404 may be representative of a smart phone,a computer system, PDA, or other electronic devices. Examples ofcomputing systems, environments, and/or configurations that mayrepresented by the data processing system 402, 404 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, network PCs, minicomputersystems, and distributed cloud computing environments that include anyof the above systems or devices.

The client computing device 102 and the server 112 may includerespective sets of internal components 402 a,b and external components404 a,b illustrated in FIG. 4. Each of the sets of internal components402 include one or more processors 420, one or more computer-readableRAMs 422, and one or more computer-readable ROMs 424 on one or morebuses 426, and one or more operating systems 428 and one or morecomputer-readable tangible storage devices 430. The one or moreoperating systems 428, the software program 108 and the templated queryoperator program 110A in the client computing device 102 and thetemplated query operator program 110B in the server 112 are stored onone or more of the respective computer-readable tangible storage devices430 for execution by one or more of the respective processors 420 viaone or more of the respective RAMs 422 (which typically include cachememory). In the embodiment illustrated in FIG. 4, each of thecomputer-readable tangible storage devices 430 is a magnetic diskstorage device of an internal hard drive. Alternatively, each of thecomputer-readable tangible storage devices 430 is a semiconductorstorage device such as ROM 424, EPROM, flash memory or any othercomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 402 a,b also includes a R/W drive orinterface 432 to read from and write to one or more portablecomputer-readable tangible storage devices 438 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the templatedquery operator program 110A, 110B, can be stored on one or more of therespective portable computer-readable tangible storage devices 438, readvia the respective R/W drive or interface 432, and loaded into therespective hard drive 430.

Each set of internal components 402 a,b also includes network adaptersor interfaces 436 such as a TCP/IP adapter cards, wireless Wi-Fiinterface cards, or 3G or 4G wireless interface cards or other wired orwireless communication links. The software program 108 and the templatedquery operator program 110A in the client computing device 102 and thetemplated query operator program 110B in the server 112 can bedownloaded to the client computing device 102 and the server 112 from anexternal computer via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 436. From the network adapters or interfaces 436, thesoftware program 108 and the templated query operator program 110A inthe client computing device 102 and the templated query operator program110B in the server 112 are loaded into the respective hard drive 430.The network may comprise copper wires, optical fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers.

Each of the sets of external components 404 a,b can include a computerdisplay monitor 444, a keyboard 442, and a computer mouse 434. Externalcomponents 404 a,b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 402 a,b also includes device drivers 440to interface to computer display monitor 444, keyboard 442, and computermouse 434. The device drivers 440, R/W drive or interface 432, andnetwork adapter or interface 436 comprise hardware and software (storedin storage device 430 and/or ROM 424).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 100 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 100 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes100 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 600provided by cloud computing environment 50 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and templated query operator importation 96.Templated query operator importation 96 may relate to utilizingtemplated query operators in the development and execution of aconversational system driven by a semantic network. More specifically,templated query operator importation 96 may allow a user query to beanalyzed for variables in the user query that can be input to atemplated query operator that may provide an accurate natural languageresponse to the user query.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A processor-implemented method for aconversational system driven by a semantic network with a library oftemplated query operators, the method comprising: loading, by aprocessor, one or more operators for the conversational system to thelibrary of templated query operators; receiving a query statement from auser; identifying an operator from the library to process the receivedquery; identifying one or more input terms for the identified operatorwithin the received query; generating one or more output terms based onprocessing the one or more identified input terms using the identifiedoperator; and generating a natural language response to the receivedquery based on the one or more generated output terms.
 2. The method ofclaim 1, wherein the one or more operators are selected from a groupconsisting of a topical question operator, a hint operator, arecommender operator, and a common relations operator.
 3. The method ofclaim 1, wherein each one or more operator has a plurality of associatedcriteria, wherein the plurality of associated criteria are selected froma group consisting of one or more input terms, one or more output terms,one or more trigger phrases that invoke the operator by an intentclassifier, one or more example operator entity mappings to train theinput collector, and one or more examples of natural language generationfrom the one or more output terms.
 4. The method of claim 1, whereinidentifying an operator from the library comprises using an intentclassifier to determine a user intent in the received query statement byapplying semantic analysis.
 5. The method of claim 1, whereinidentifying the one or more input terms comprises using an inputcollector to identify the one or more input terms.
 6. The method ofclaim 1, wherein generating the natural language response comprisesusing a natural language generator to analyze the one or more outputterms.
 7. The method of claim 1, further comprising: presenting thegenerated natural language response to the user, wherein the generatednatural language response is presented on as a plurality of text on agraphical user interface or as a plurality of human speech through aspeaker using text-to-speech technology.
 8. A computer system for aconversational system driven by a semantic network with a library oftemplated query operators, the computer system comprising: one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable tangible storage media, and program instructionsstored on at least one of the one or more tangible storage media forexecution by at least one of the one or more processors via at least oneof the one or more memories, wherein the computer system is capable ofperforming a method comprising: loading one or more operators for theconversational system to the library of templated query operators;receiving a query statement from a user; identifying an operator fromthe library to process the received query; identifying one or more inputterms for the identified operator within the received query; generatingone or more output terms based on processing the one or more identifiedinput terms using the identified operator; and generating a naturallanguage response to the received query based on the one or moregenerated output terms.
 9. The computer system of claim 8, wherein theone or more operators are selected from a group consisting of a topicalquestion operator, a hint operator, a recommender operator, and a commonrelations operator.
 10. The computer system of claim 8, wherein each oneor more operator has a plurality of associated criteria, wherein theplurality of associated criteria are selected from a group consisting ofone or more input terms, one or more output terms, one or more triggerphrases that invoke the operator by an intent classifier, one or moreexample operator entity mappings to train the input collector, and oneor more examples of natural language generation from the one or moreoutput terms.
 11. The computer system of claim 8, wherein identifying anoperator from the library comprises using an intent classifier todetermine a user intent in the received query statement by applyingsemantic analysis.
 12. The computer system of claim 8, whereinidentifying the one or more input terms comprises using an inputcollector to identify the one or more input terms.
 13. The computersystem of claim 8, wherein generating the natural language responsecomprises using a natural language generator to analyze the one or moreoutput terms.
 14. The computer system of claim 8, further comprising:presenting the generated natural language response to the user, whereinthe generated natural language response is presented on as a pluralityof text on a graphical user interface or as a plurality of human speechthrough a speaker using text-to-speech technology.
 15. A computerprogram product for a conversational system driven by a semantic networkwith a library of templated query operators, the computer programproduct comprising: one or more computer-readable tangible storage mediaand program instructions stored on at least one of the one or moretangible storage media, the program instructions executable by aprocessor of a computer to perform a method, the method comprising:loading one or more operators for the conversational system to thelibrary of templated query operators; receiving a query statement from auser; identifying an operator from the library to process the receivedquery; identifying one or more input terms for the identified operatorwithin the received query; generating one or more output terms based onprocessing the one or more identified input terms using the identifiedoperator; and generating a natural language response to the receivedquery based on the one or more generated output terms.
 16. The computerprogram product of claim 15, wherein the one or more operators areselected from a group consisting of a topical question operator, a hintoperator, a recommender operator, and a common relations operator. 17.The computer program product of claim 15, wherein each one or moreoperator has a plurality of associated criteria, wherein the pluralityof associated criteria are selected from a group consisting of one ormore input terms, one or more output terms, one or more trigger phrasesthat invoke the operator by an intent classifier, one or more exampleoperator entity mappings to train the input collector, and one or moreexamples of natural language generation from the one or more outputterms.
 18. The computer program product of claim 15, wherein identifyingan operator from the library comprises using an intent classifier todetermine a user intent in the received query statement by applyingsemantic analysis.
 19. The computer program product of claim 15, whereinidentifying the one or more input terms comprises using an inputcollector to identify the one or more input terms.
 20. The computerprogram product of claim 15, wherein generating the natural languageresponse comprises using a natural language generator to analyze the oneor more output terms.